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Support Vector Machines
Pattern RecognitionSergios TheodoridisKonstantinos Koutroumbas
Second Edition
A Tutorial on Support Vector Machines for Pattern RecognitionData Mining and Knowledge Discovery, 1998C. J. C. Burges
Separable Case
Maximum Margin Formulation
Separable Case
Label the training data
Hyperplane satisfyw: normal to the hyperplane |b|/||w||: perpendicular distance from the
hyperplane to the origin
d+ (d-):margin
diiii xyliyx R},1,1{,,...,1},,{
0xw bg(x)
Separable Case
d+
d-
positive example
negative example
Separable Case
Suppose that all the training data satisfy the following constraints
These can be combines into one set of inequalities
Distance of a point from a hyperplane
1for1wx ii yb1for1wx ii yb
iby ii ,01)wx(
||||
1
wdd
class 1
class 2
Separable Case
Having a margin of
Task compute the parameter w, b of the hyperplane
||||
2
||||
1
||||
1
www maximize
2||||2
1)(minimize wwJ
ibxy ii 01)w(tosubject
Separable Case
Karush-Kuhn-Tucker (KKT) conditions
: vector of the Langrange multiplier : Langrangian function
0),,( bwLw
0),,( bwLb
Nii ,....,2,1,0
),,( bwL
Nibwxy iii ,...,2,1,0]1)([
N
iiii bwxywbwL
1
2 ]1)([||||2
1),,(
N
iiii xyw
1
N
iii y
1
0
Separable Case
Wolfe dual representation form
),,(maximize bwL
N
iiii xyw
1
tosubject 01
N
iii y 0i
N
i jij
Tijijii xxyy
1 ,2
1max
N
iiii y
1
0,0tosubject
Image Categorization by Learning and Image Categorization by Learning and Reasoning with RegionsReasoning with Regions
Yixin ChenUniversity of New Orleans
James Z. WangThe Pennsylvania State University
Journal of Machine Learning Research 5 (2004)Journal of Machine Learning Research 5 (2004)(Submitted 7/03; Revised 11/03; Published 8/04)(Submitted 7/03; Revised 11/03; Published 8/04)
Introduction
Automatic image categorization Difficulties
Variable & uncontrolled image conditions Complex and hard-to-describe objects in image Objects occluding other objects
Applications Digital libraries, Space science, Web searching,
Geographic information systems, Biomedicine, Surveillance and sensor system, Commerce, Education
Overview
Give a set of labeled images, can a computer program learn such knowledge or semantic concepts form implicit information of objects contained in image?
Related Work
Multiple-Instance LearningDiverse Density Function (1998)MI-SVM (2003)
Image CategorizationColor Histograms (1998-2001)Subimage-based Methods
(1994-2004)
Motivation
Correct categorization of an image depends on identifying multiple aspects of the image
Extension of MIL→A bag must contain a number of instances satisfying various properties
A New Formulation of Multiple-Instance Learning
Maximum margin problem in a new feature space defined by the DD function
DD-SVMIn the instance feature space, a
collection of feature vectors, each of which is called an instance prototype, is determined according to DD
A New Formulation of Multiple-Instance Learning
Instance prototype:• A class of instances (or regions) that is
more likely to appear in bags (or images) with the specific label than in the other bags
Maps every bag to a point in bag feature space
Standard SVMs are the trained in the bag feature space
Outline
Image segmentation & feature representation
DD-SVM, and extension of MIL Experiments & result Conclusions & future work
Image Segmentation
Partitions the image into non-overlapping blocks of size 4x4 pixels
Each feature vector consists of six featuresAverage color components in a block
• LUV color spaceSquare root of the second order
moment of wavelet coefficients in high-frequency bands
Image Segmentation
Daubechies-4 wavelet transform
Moments of wavelet coefficients in various frequency bands are effective for representing texture (Unser, 1995)
LL HL
HHLH
k, l2x2 coefficients
2
11
0
1
0
2,4
1
i jjlikcf
Image Segmentation
k-means algorithm: cluster the feature vectors into several classes with every class corresponding to one “region”
Adaptively select N by gradually increasing N until a stopping criterion is met (Wang et al. 2001)
Segmentation Results
Image Representation
:the mean of the set of feature vectors corresponding to each region Rj
Shape properties of each regionNormalized inertia of order 1, 2, 3
(Gersho, 1979)
j
f
21
Rrr-r
γ),R(
j
j
V
Ij
Image Representation
Shape feature of region Rj as
An image Bi
Segmentation: {Rj : j = 1, …, Ni}
Feature vectors: { xij : j = 1, …, Ni}
321
)3,R(,
)2,R(,
)1,R(
I
jI
I
jI
I
jIs j
T
Tj
T
jij sf
,x 9-dimensional feature vector
An extension of Multiple-Instance Learning
Maximum margin formulation of MIL in a bag feature space
Constructing a bag feature spaceDiverse densityLearning instance prototypesComputing bag features
Maximum Margin Formulation of MIL in a Bag Feature Space
Basic idea of new MIL framework:Map every bag to a point in a new
feature space, named the bag feature space
To train SVMs in the bag feature space
l
jijijiji
l
ii Kyy
i 1,1
))B(),B((2
1maxarg*
liC
y
i
l
iii
,...,1,0
01
subject to
Constructing a Bag Feature Space
Clues for classifier design:What is common in positive bags and
does not appear in the negative bagsInstance prototypes computed from the
DD function A bag feature space is then constructed
using the instance prototypes
Diverse Density (Maron and Lozano-Perez, 1998)
A function defined over the instance space
DD value at a point in the feature spaceThe probability that the point agrees
with the underlying distribution of positive and negative bags
Diverse Density
It measures a co-occurrence of instances from different (diverse) positive bags
2
wxx
111
2
1)wx,( jiey
yDD
Ni
ji
il
iD
2
1
2T xDiag(w)xx
w
Learning Instance Prototype
An instance prototype represents a class of instances that is more likely to appear in positive bags than in negative bags
Learning instance prototypes then becomes an optimization problemFinding local maximizers of the DD fu
nction in a high-dimensional
Learning Instance Prototype
How do we find the local maximizers?Start an optimization at every instance
in every positive bag
Constraints:Need to be distinct from each otherHave large DD values
Computing Bag Features
Let be the collection of instance prototypes
Bag features,
},...,1:)w,x{( ** nkkk
},...,1:x{B),B( iijii Nj
*
*2
*1
*,...,1
*2,...,1
*1,...,1
xxmin
...
xxmin
xxmin
)B(
ni
i
i
wnijNj
wijNj
wijNj
i
Experimental Setup for Image Categorization
COREL Corp: 2,000 images20 image categoriesJPEG format, size 384*256 (256*384)Each category are randomly divided
into a training set and a test set (50/50)SVMLight [Joachims, 1999] software is used
to train the SVMs
Sample Images (COREL)
Image Categorization Performance
5 random test sets, 95% confidence intervals The images belong to Cat.0 ~ Cat.9
14.8%
6.8%
Chapelle et al., 1999
Andrews et al., 2003
Image Categorization Experiments
Sensitivity to Image Segmentation
k-means clustering algorithmwith 5 different stopping criteria
1,000 images for Cat.0 ~ Cat.9
Robustness to Image Segmentation
6.8% 9.5% 11.7% 13.8%
27.4%
Robustness to the Number of Categories in a Data Set
81.5%
67.5%
6.8%
12.9
Difference in Average Classification accuracies
Sensitivity to the Size of Training Images
Sensitivity to the Diversity of Training Images Varies
MUSK Data Sets
Speed
40 minutes Training set of 500 images (4.31 regions per
image) Pentium III 700MHz PC running the Linux op
erating system Algorithm is implemented in Matlab, C progr
amming language The majority is spent on learning
instance prototypes
Conclusions
A region-based image categorization method using an extension of MIL → DD-SVM
Image → collection of regions → k-means alg. Image → a point in a bag feature space
(defined by a set of instance prototypes learned with the DD func.)
SVM-based image classifiers are trained in the bag feature space
DD-SVM outperforms two other methods DD-SVM generates highly competitive results
on MUSK data set
Future Work
Limitations Region naming (Barnard et al., 2003)
Texture dependence Improvement
Image segmentation algorithm DD function
Scene category can be a vector Semantically-adaptive searching Art & biomedical images